Hierarchical Bayesian Inference and Learning in Spiking Neural Networks

Numerous experimental data from neuroscience and psychological science suggest that human brain utilizes Bayesian principles to deal the complex environment. Furthermore, hierarchical Bayesian inference has been proposed as an appropriate theoretical framework for modeling cortical processing. Howev...

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Veröffentlicht in:	IEEE transactions on cybernetics Jg. 49; H. 1; S. 133 - 145
Hauptverfasser:	Guo, Shangqi, Yu, Zhaofei, Deng, Fei, Hu, Xiaolin, Chen, Feng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Bayes methods Bayesian analysis Biological neural networks Biological system modeling Brain Computational modeling Digits Handwriting Hidden Markov models Hierarchical Bayesian model Integrated circuit modeling Machine learning mean field theory Neural networks Neurons spike-timing-dependent plasticity (STDP) Spiking spiking neural network Statistical inference variational expectation maximization winner-takes-all (WTA) circuits
ISSN:	2168-2267, 2168-2275, 2168-2275
Online-Zugang:	Volltext
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